Factors Influencing Students' Behavioral Intention to Continue Artificial Intelligence Learning

Chai, Ching Sing; Lin, Pei-Yi; Jong, Morris Siu‐Yung; Dai, Yun; Chiu, Thomas K. F.; Huang, Biyun

doi:10.1109/iset49818.2020.00040

Cited by 58 publications

(93 citation statements)

References 25 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The analysis showed that all the technology teachers expressed that students should learn more about AI due to its large impact on our everyday life, and there is a need for an AI curriculum. This is aligned with the important global strategic initiative in educating the next generation by including AI topics in school curricula [3] and the students' motivation to learn AI technologies [22,23]. This subtheme is illustrated in the below excerpts.…”

Section: Result Discussion and Conclusionmentioning

confidence: 80%

Sustainable Curriculum Planning for Artificial Intelligence Education: A Self-Determination Theory Perspective

2020

Self Cite

View full text Add to dashboard Cite

The teaching of artificial intelligence (AI) topics in school curricula is an important global strategic initiative in educating the next generation. As AI technologies are new to K-12 schools, there is a lack of studies that inform schools’ teachers about AI curriculum design. How to prepare and engage teachers, and which approaches are suitable for planning the curriculum for sustainable development, are unclear. Therefore, this case study aimed to explore the views of teachers with and without AI teaching experience on key considerations for the preparation, implementation and continuous refinement of a formal AI curriculum for K-12 schools. It drew on the self-determination theory (SDT) and four basic curriculum planning approaches—content, product, process and praxis—as theoretical frameworks to explain the research problems and findings. We conducted semi-structured interviews with 24 teachers—twelve with and twelve without experience in teaching AI—and used thematic analysis to analyze the interview data. Our findings revealed that genuine curriculum creation should encompass all four forms of curriculum design approach that are coordinated by teachers’ self-determination to be orchestrators of student learning experiences. This study also proposed a curriculum development cycle for teachers and curriculum officers.

show abstract

Section: Result Discussion and Conclusionmentioning

confidence: 80%

Sustainable Curriculum Planning for Artificial Intelligence Education: A Self-Determination Theory Perspective

2020

Self Cite

View full text Add to dashboard Cite

show abstract

“…Students with the ability to evaluate and create AI could infer from, connect, manipulate, and categorize AI concepts together in novel ways. Overall, although these articles showed slight variations on the definition of AI literacy, they support the notion that everyone, especially K-12 children, acquire basic AI knowledge and abilities, and enhance motivation and career interest(Chai et al, 2020b). In addition to knowing and using AI ethically, AI literacy serves as a set of competencies that enables individuals to critically evaluate AI technologies, communicate and collaborate effectively with AI(Long & Magerko, 2020).…”

mentioning

confidence: 86%

AI Literacy: Definition, Teaching, Evaluation and Ethical Issues

Leung

Chu

et al. 2021

Proceedings of the Association for Information Science and Tech

115

View full text Add to dashboard Cite

Artificial Intelligence (AI) is at the top of the agenda for education leaders today in educating the next generation across the globe. However, public understanding of AI technologies and how to define AI literacy is under-explored. This vision poses upcoming challenges for our next generation to learn about AI. On this note, an exploratory review was conducted to conceptualize the newly emerging concept "AI literacy", in search for a sound theoretical foundation to define, teach and evaluate AI literacy. Grounded in literature on 18 existing peer-reviewed articles, this review proposed four aspects (i.e, know and understand, use, evaluate, and ethical issues) for fostering AI literacy based on the adaptation of classic literacies. This study sheds light on the consolidated definition, teaching, and ethical concerns on AI literacy, establishing the groundwork for future research such as competency development and assessment criteria on AI literacy.

show abstract

“…4, n = 13 (Sperling & Lickerman, 2012;Burgsteiner et al, 2016;Mariescu-Istodor and Jormanainen, 2019;Kahn et al, 2018;Wan et al, 2020;Rodríguez-García et al, 2020aEssinger & Rosen, 2011;Ossovski & Brinkmeier, 2019;Evangelista et al, 2018;Vachovsky et al, 2016;Estevez et al, 2019). Eight studies focused on primary schoolers (Mariescu-Istodor and Jormanainen, 2019; Lee et al, 2020;Ho & Scadding, 2019;Toivonen, et al, 2020;Chai, et al, 2020;Druga et al, 2019;Tedre, et al, 2020;Hitron, et al, 2018) while only two studies were found that targets Kindergarten (Williams et al, 2019a(Williams et al, , 2019b. Four studies focused each on elementary, middle (Sabuncuoglu, 2020;Rodríguez-García et al, 2020aSakulkueakulsuk et al, 2018), middle/high (Opel et al, 2019;Zimmermann-Niefield et al, 2019aZimmermann-Niefield, et al, 2020) and teachers ( Chiu & Chai, 2020;Kandlhofer et al, 2019;Zhou, et al, 2021) and only one that covers all levels from elementary to high school.…”

Section: Articles Distributed Based On Educational Levelsmentioning

confidence: 99%

“…It can be deduced from the literature that ML has been taught across K-12 levels from primary to high school. These have also been carried out in various settings such as in laboratory (Essinger & Rosen, 2011), classroom (Chai et al, 2020), summer camp , workshop (e.g., Druga et al, 2019) and at home (Vartiainen et al, 2020a(Vartiainen et al, , 2020b. The objective of the identified studies focuses on how to teach central machine learning concepts with a paper (Vachovsky et al, 2016) concerned with increasing girls' interest in AI.…”

Section: Pedagogical Developmentmentioning

confidence: 99%

A systematic review of teaching and learning machine learning in K-12 education

et al. 2022

View full text Add to dashboard Cite

The increasing attention to Machine Learning (ML) in K-12 levels and studies exploring a different aspect of research on K-12 ML has necessitated the need to synthesize this existing research. This study systematically reviewed how research on ML teaching and learning in K-12 has fared, including the current area of focus, and the gaps that need to be addressed in the literature in future studies. We reviewed 43 conference and journal articles to analyze specific focus areas of ML learning and teaching in K-12 from four perspectives as derived from the data: curriculum development, technology development, pedagogical development, and teacher training/professional development. The findings of our study reveal that (a) additional ML resources are needed for kindergarten to middle school and informal settings, (b) further studies need to be conducted on how ML can be integrated into subject domains other than computing, (c) most of the studies focus on pedagogical development with a dearth of teacher professional development programs, and (d) more evidence of societal and ethical implications of ML should be considered in future research. While this study recognizes the present gaps and direction for future research, these findings provide insight for educators, practitioners, instructional designers, and researchers into K-12 ML research trends to advance the quality of the emerging field.

show abstract

Factors Influencing Students' Behavioral Intention to Continue Artificial Intelligence Learning

Cited by 58 publications

References 25 publications

Sustainable Curriculum Planning for Artificial Intelligence Education: A Self-Determination Theory Perspective

Sustainable Curriculum Planning for Artificial Intelligence Education: A Self-Determination Theory Perspective

AI Literacy: Definition, Teaching, Evaluation and Ethical Issues

A systematic review of teaching and learning machine learning in K-12 education

Contact Info

Product

Resources

About